This paper is motivated by a wide range of background correction problems in gene array data analysis, where the raw gene expression intensities are measured with error. Estimating a conditional density function from the contaminated expression data is a key aspect of statistical inference and visualization in these studies. We propose re-weighted deconvolution kernel methods to estimate the conditional density function in an additive error model, when the error distribution is known as well as when it is unknown. Theoretical properties of the proposed estimators are investigated with respect to the mean absolute error from a "double asymptotic" view. Practical rules are developed for the selection of smoothing-parameters. Simulated examples and an application to an Illumina bead microarray study are presented to illustrate the viability of the methods.

中文翻译：

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测量误差问题中的条件密度估计

本文受到基因阵列数据分析中广泛的背景校正问题的启发，其中原始基因表达强度的测量存在误差。从受污染的表达数据中估计条件密度函数是这些研究中统计推断和可视化的一个关键方面。我们提出重新加权反卷积核方法来估计加性误差模型中的条件密度函数，当误差分布已知以及未知时。从“双渐近”的角度，针对平均绝对误差来研究所提出的估计量的理论特性。为选择平滑参数制定了实用规则。